Systems and methods for detecting strokes

ABSTRACT

A system for detecting a type of stroke includes a processing circuit. The processing circuit is configured to receive heart data regarding a heart rhythm of a patient and physiological data regarding a physiological characteristic of the patient. The heart data is indicative of an occurrence of atrial fibrillation and the physiological data is indicative of an occurrence of a stroke. The processing circuit is further configured to determine a likelihood that the stroke was an embolic stroke based on the heart data and to provide an output including an indication of the likelihood that the stroke was an embolic stroke.

BACKGROUND

A stroke is the disturbance in blood supply to the brain. Two majorcategories of strokes are ischemic strokes and hemorrhagic strokes.Ischemic strokes are caused when blood supply to part of the brain isdecreased, thereby depriving the brain of oxygen and destroying braintissue. Ischemic strokes may be caused by thrombosis (obstruction of ablood vessel by a blood clot forming in the blood vessel), embolism(obstruction of a blood vessel by an embolus traveling from elsewhere inthe body, also known as an “embolic stroke”), systematic hypoperfusion(general decrease in blood supply, e.g., due to shock), venousthrombosis (obstruction of a blood vessel in the sinuses which drainsblood from the brain). Hemorrhagic strokes are caused by a rupturedartery in the brain, which causes blood to pool and destroy braintissue. Both ischemic and hemorrhagic strokes may cause severe deficitsin a patient, therefore a patient that has experienced a stroke shouldseek medical treatment as soon as possible. Other medical conditions,such as atrial fibrillation, may cause a patient to be particularlysusceptible to certain stroke types, such as embolic strokes.

Due to the different etiologies of ischemic and hemorrhagic strokes,each stroke type requires different treatment plans. For example,strokes caused by clotting (e.g., thrombolytic and embolic strokes) maybe treated with a “clot busting” medication that should be administeredwithin the first three hours of the onset of a stroke. When a stroke isproperly treated, a patient's mental and physical deficits may beminimized. Misguided treatment plan (e.g., upon misdiagnosing a stroke)could progress the stroke and possibly lead to death.

SUMMARY

One embodiment relates to a system for detecting a type of stroke thatincludes a processing circuit. The processing circuit is configured toreceive heart data regarding a heart rhythm of a patient andphysiological data regarding a physiological characteristic of thepatient. The heart data is indicative of an occurrence of atrialfibrillation and the physiological data is indicative of an occurrenceof a stroke. The processing circuit is further configured to determine alikelihood that the stroke was an embolic stroke based on the heart dataand to provide an output including an indication of the likelihood thatthe stroke was an embolic stroke.

Another embodiment relates to a method for detecting a type of stroke.The method includes receiving heart data regarding a heart rhythm of thepatient and receiving physiological data regarding a physiologicalcharacteristic of the patient. The physiological data is indicative ofan occurrence of a stroke and the heart data is indicative of anoccurrence of atrial fibrillation prior to the stroke. The methodfurther includes determining a likelihood that the stroke was an embolicstroke based on the heart data.

Another embodiment relates to a system for detecting a type of strokethat includes a heart-monitoring device and a processing circuit. Theheart-monitoring device is configured to acquire heart data regarding aheart rhythm of a patient. The heart data is indicative of an occurrenceof atrial fibrillation and the heart-monitoring device acquires heartdata using micro impulse radar. The processing circuit is configured toreceive the heart data and physiological data. The physiological dataregards a physiological characteristic of the patient and is indicativeof the occurrence of a stroke. The processing circuit is furtherconfigured to determine a likelihood that the stroke was an embolicstroke based on the physiological data and the heart data, and tocontrol operation of an output device to provide the output, wherein theoutput is based on the likelihood that the stroke was an embolic stroke.

Another embodiment relates to a method for detecting a stroke. Themethod includes receiving heart data regarding a heart rhythm of apatient and receiving physiological data regarding a physiologicalcharacteristic of the patient. The heart data is indicative of anoccurrence of atrial fibrillation and the physiological data isindicative of an occurrence of a stroke after the occurrence of theatrial fibrillation. The method further includes determining alikelihood that the stroke was an embolic stroke based on thephysiological data and the heart data, and providing an output, whereinthe output is based on the likelihood that the stroke was an embolicstroke.

Another embodiment relates to a device for detecting a stroke thatincludes a heart-monitoring sensor, a physiological sensor, and a strokedetection module. The heart-monitoring sensor is configured to monitor aheart rhythm of a patient, and monitoring the heart rhythm includesacquiring heart data. The physiological sensor is configured to monitora physiological characteristic of the patient, and monitoring thephysiological characteristic includes acquiring physiological data. Thestroke detection module includes a processing circuit configured todetermine whether the patient experienced a stroke based on themonitored physiological characteristic; determine whether the patientexperienced atrial fibrillation based on the monitored heart rhythm;determine whether the patient experienced an embolic stroke based on thepatient experiencing a stroke and atrial fibrillation within apredetermined time period; and control operation of the device toprovide an output, wherein the output is based on the determination ofwhether the patient experienced an embolic stroke.

Another embodiment relates to a method for detecting a medical event.The method includes monitoring a heart rhythm of a patient andmonitoring a physiological characteristic of the patient. Monitoring theheart rhythm includes acquiring heart data and monitoring thephysiological characteristic includes acquiring physiological data. Themethod further includes determining whether the patient experienced astroke based on the monitored physiological characteristic; determiningwhether the patient experienced atrial fibrillation based on themonitored heart rhythm; determining whether the patient experienced anembolic stroke based on the patient experiencing a stroke and atrialfibrillation within a predetermined time period; and providing anoutput, including whether the patient experienced an embolic stroke.

Another embodiment relates to a system for determining a probability ofa patient experiencing a stroke that includes a heart-monitoring sensorand a processing circuit. The heart-monitoring sensor is configured tomonitor a heart rhythm of the patient. The processing circuit isconfigured to receive heart data from the heart-monitoring sensor,wherein the heart data is based on the monitored heart rhythm, andwherein the heart data is indicative of whether the patient experiencingatrial fibrillation; determine a probability of the patient experiencinga future stroke based on the heart data; and provide an output based onthe probability of the patient experiencing a future stroke.

Another embodiment of the invention relates to a method for determininga probability of a patient experiencing a stroke. The method includesreceiving heart data, wherein the heart data is based on a monitoredheart rhythm, and wherein the heart data is indicative of the patientexperiencing atrial fibrillation; determining a probability of thepatient experiencing a future stroke based on the heart data; andproviding an output based on the probability of the patient experiencinga future stroke.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of a home system for detecting a stroke,according to one embodiment.

FIG. 2 is an illustration of a system for detecting a stroke, accordingto one embodiment.

FIG. 3A is an illustration of a wearable stroke detection device,according to one embodiment.

FIG. 3B is an illustration of a wearable stroke detection device,according to another embodiment.

FIG. 3C is an illustration of a wearable stroke detection device,according to another embodiment.

FIG. 4 is a diagram of a method for detecting a stroke, according to oneembodiment.

FIG. 5 is a diagram of a method for detecting a stroke, according toanother embodiment.

FIG. 6 is a diagram of a method for detecting a stroke, according to oneembodiment.

FIG. 7 is a diagram of a method for detecting a medical event, accordingto one embodiment.

FIG. 8 is a diagram of a method for determining a probability of apatient experiencing a stroke, according to one embodiment.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and claims are not meant to be limiting.Other embodiments may be utilized, and other changes may be made,without departing from the scope of the subject matter presented here.

Referring generally to the figures, various embodiments of systems,methods, and computer readable media for detecting strokes are shown anddescribed. Strokes remain one of the leading causes of death in theworld. Furthermore, as people age, the likelihood of experiencing astroke increases. Heart fibrillation (e.g., proximal atrialfibrillation) is a common precursor of certain types of strokes. Forexample, an embolic stroke is often preceded by atrial fibrillation. Insome cases, atrial fibrillation may occur one-half to three hours beforean emboli is ejected from the left ventricle of a person's heart and, insome cases, travels to the brain, thereby blocking a blood vessel in thebrain and causing a thrombotic (or embolic) stroke.

Strokes may be diagnosed through several techniques, including aneurological examination, CT scans, MRI scans, Doppler ultrasound, andarteriography, among other techniques. Physical examinations may also beused to diagnose a stroke, including examinations relating to thepatient's level of consciousness, horizontal eye movement, visual fieldtests, facial palsy, movement of the limbs (e.g., motor arm tests, motorleg tests, etc.), limb ataxia, sensory tests (e.g., responsiveness totouches or pinpricks, etc.), language skills (e.g., describing apicture, reading sentences and identifying objects, etc.), and motorskills (e.g., ability to speak coherently, including motor control ofthe tongue, lips, throat, and lungs), among others. Other signs of astroke include, for example, slurred speech, new onset of weakness inarm or leg (most commonly in one side of body), and a degraded abilityto walk. Such physiological characteristics are detectable using sensorsand other monitoring devices and technologies, such as microphones,cameras (e.g., video cameras, infrared cameras, etc.), scanning devices,sonar, radar, motion sensors, pressure sensors (e.g., to measure bloodpressure, etc.), and thermometers, among others.

As discussed in further detail in relation to the various embodimentsdisclosed herein, a stroke detection device may determine whether apatient experiences a stroke, including an embolic stroke, or thelikelihood that the patient experienced an embolic stroke, based onwhether the patient experienced atrial fibrillation at some time beforeor during the stroke. Several factors may be considered in making such adetermination, including whether the patient experienced atrialfibrillation before stroking, features of the atrial fibrillationepisode that the patient experiences (e.g., magnitude, duration, numberof episodes, etc.), a duration between experiencing atrial fibrillationand stroking or duration after experiencing atrial fibrillation andstroking, and so on. Other factors and determinations may also be usedin determining the likelihood of whether a patient experienced anembolic stroke, such as the patient's CHADS₂ score (i.e., a clinicalprediction tool used to estimate the risk of stroke in patients based onother medical conditions), CHA₂DS₂-VAS_(C) score (a variation of theCHADS₂ scoring methodology that includes additional stroke riskfactors), and other medical history.

Referring now to FIG. 1, an illustration of home system 100 fordetecting a stroke is shown, according to one embodiment. System 100 mayinclude various devices, including furniture-mounted device 112,wearable device 114, and wall-or-ceiling-mounted device 116. In someembodiments, system 100 includes multiple devices, such asfurniture-mounted device 112, wearable device 114 worn by patient 110,wall-or-ceiling-mounted device 116, and other devices, although system100 may include a single device, such as wearable device 114. Thedevices may be configured to monitor patient 110. For example, thedevices may be configured to monitor the heart rhythm or physiologicalcharacteristics of patient 110 to detect when patient 110 experiences astroke, if patient 110 has already had a stroke, the likelihood thatpatient 110 will have a stroke, and so on, as will be discussed infurther detail below. In multiple-device systems, the devices maycommunicate with one another, be configured to operate together, or beconfigured to operate independently of one another. Furniture-mounteddevice 112 may be configured to be affixed to furniture or otherobjects, such as appliances, toys, equipment, or other household items(e.g., vacuum cleaners, pets, robots, laundry baskets, etc.). As will bediscussed in further detail below, the devices may be configured tocommunicate with systems or devices not shown or described in FIG. 1,such as emergency medical systems, medical providers, health monitoringstations, mobile devices, cellular systems, computer networks,communication networks, devices associated with a family member oremergency contact, among others.

Referring to FIG. 2, an illustration of system 200 for detecting astroke is shown according to one embodiment. System 200 may includepatient monitoring sensor, such as stroke detection device 210,heart-monitoring sensor 220, and physiological sensor 230. Devices usedin home system 100, including furniture mounted device 112, wearabledevice 114, and wall-or-ceiling-mounted device 116, may be the same asor similar to system 200, or include components thereof, such as strokedetection device 210. System 200 may include any number of strokedetection devices 210, heart-monitoring sensors 220, and physiologicalsensors 230, as well as other elements not pictured in FIG. 2. Strokedetection device 210 may receive inputs from heart-monitoring sensor 220and physiological sensor 230. For example, in some embodiments, strokedetection device 210 receives heart data from heart-monitoring sensor220 and/or physiological data from physiological sensor 230. The heartdata may include information relating to a heart rhythm of patient 110.For example, the heart data may be indicative of the fibrillation of theheart of patient 110, including whether patient 110 experiences atrialfibrillation. The physiological data may include information relating toa physiological characteristic of patient 110, including whether aphysiological characteristic of patient 110 changes or otherwiseindicates that patient 110 experiences a stroke. In some embodiments,sensors of system 200, such as physiological sensor 230 andheart-monitoring sensor 220, may be configured to continuously operateor monitor patient 110 or intermittently operate or monitor patient 110.For example, heart-monitoring sensor 220 may be included in system 100(e.g., wall-or-ceiling-mounted device 116) and may remotely monitorpatient 110 using micro-impulse radar. In some embodiments, sensors ofsystem 200 may “sleep” or operate in a passive mode and may becomeactive upon receiving an input (e.g., from processing circuit 201). Forexample, in one embodiment, processing circuit 201 may causephysiological sensor 230 to become activated based on determining thatpatient 110 is at a high risk of experiencing a stroke. In anotherexample, processing circuit 201 may cause physiological sensor 230 toacquire data more frequently based on, for example, determining thatpatient 110 is at a high risk of stroking or has exceeded a strokeprobability threshold. In an embodiment, processing circuit 201 mayreceive input from heart-monitoring sensor 220 indicative of an atrialfibrillation event and determine, responsive to receiving the inputindicative of an atrial fibrillation event, that the patient's risk ofstroking has increased or has exceeded a stroke probability threshold.In some embodiments, the stroke probability threshold may be based on apredetermined likelihood. The stroke probability threshold may be basedon a determined likelihood, a learned likelihood, or a likelihoodselected by patient 110. In some embodiments, system 200 may provide anoutput or otherwise communicate with another system (e.g., emergencyservice provider system, medical provider, the patient, etc.) based ondetermining that patient 110 is at a high risk of stroking or hasexceeded a stroke likelihood threshold.

In some embodiments, stroke detection device 210 includes processingcircuit 201, memory 203, input/output device 205, and communicationsinterface 207. Processing circuit 201 may be implemented as one or moremicroprocessing elements, digital signal processing elements,application specific integrated circuit (ASIC) elements, fieldprogrammable gate array (FPGA) elements, groups of processingcomponents, intellectual property cores, or other suitable electronicprocessing components configured on a single integrated circuit or onmultiple integrated circuits. Memory 203 may be one or more devices(e.g., RAM, ROM, Flash memory, hard disk storage, etc.) for storing dataand/or computer code for completing and/or facilitating the variousprocesses described herein. Memory 203 may be or include non-transientvolatile memory, non-volatile memory, and non-transitory computerstorage media. Memory 203 may include data base components, object codecomponents, script components, or any other type of informationstructure for supporting the various activities and informationstructures described herein. Memory 203 may be communicably connected toone or more processors and include computer code or instructions forexecuting one or more processes described herein.

Processing circuit 201 may be configured to receive data, makedeterminations, and provide outputs, according to some embodiments.Processing circuit 201 may be configured to receive heart data andphysiological data, for example, from heart-monitoring sensor 220 andphysiological sensor 230, or via a report from a human observing thepatient. The received heart data may regard a heart rhythm of patient110. The heart data may further be indicative of an occurrence of atrialfibrillation. For example, the heart data may include informationregarding the magnitude and duration of heart fibrillation of patient110. The received physiological data may regard a physiologicalcharacteristic of patient 110. The received physiological data may beindicative of an occurrence of a stroke. The received physiological datamay be indicative of a time associated with a stroke, such as the timeat which the data was collected or delivered, the time the strokeoccurred, or the time at which the stroke was detected. For example, thephysiological data may include information relating to cranial bloodpressure of patient 110.

In some embodiments, processing circuit 201 is configured to determine alikelihood that a stroke was an embolic stroke. Processing circuit 201may determine the likelihood based on received heart data, based onreceived physiological data, based on both the received heart data andthe receive physiological data, and so on. In some embodiments,processing circuit 201 may determine whether patient 110 experiences orexperienced atrial fibrillation. For example, processing circuit 201 maydetermine if patient 110 is experiencing atrial fibrillation based on amonitored heart rhythm (e.g., beats per minute, duration between beats,magnitude of fibrillation, etc.). Processing circuit 201 may determinewhether patient 110 experienced an embolic stroke based on other factorsas well, including whether patient 110 experienced a stroke and atrialfibrillation within a predetermined time period. For example, because aperson may be more likely to experience an embolic stroke within threehours after experiencing atrial fibrillation, processing circuit 201 maydetermine that patient 110 likely experienced an embolic stroke based ondetermining that patient 110 experienced a stroke within three hours ofexperiencing atrial fibrillation. In some embodiments, the time periodmay be greater than or less than three hours (e.g., eight hours,overnight, until the following day, until a visit with a medicalprovider such as a doctor, etc.). In some embodiments, the time periodmay be based on the condition of patient 110, including a conditionmonitored by physiological sensor 230, or a fibrillation featuremonitored by heart-monitoring sensor 220. In some embodiments,processing circuit 201 may be configured to operate differentlydepending on the likelihood of patient 110 experiencing a stroke. Forexample, processing circuit 201 may be configured to treat a fallfollowed by inactivity as a likely stroke indicator, and may weigh suchfactors differently depending on the likelihood of patient 110experiencing a stroke (e.g., following an atrial fibrillation event).

In some embodiments, processing circuit 201 is configured to provide anoutput. Processing circuit 201 may be configured to provide an outputindicating whether patient 110 experienced a stroke. In someembodiments, processing circuit 201 is configured to control operationof stroke detection device 210 to provide an output. In someembodiments, the output is based on the likelihood that the stroke wasan embolic stroke. In some embodiments, the output is based on adetermination of whether patient 110 experienced an embolic stroke. Insome embodiments, the output is based on a monitored physiologicalcharacteristic or heart data (e.g., via physiological sensor 230 andheart-monitoring sensor 220, respectively). For example, the output maybe based on whether patient 110 experienced atrial fibrillation (e.g.,providing a warning that patient 110 is at a heightened risk ofexperiencing a stroke, and/or that such a stroke has a higher likelihoodof being an embolic stroke than a hemorrhagic stroke).

Processing circuit 201 may be configured to control various componentsof stroke detection device 210 or stroke detection system 200. Forexample, in some embodiments, physiological sensor 230 is configured toactivate only upon receiving instructions from processing circuit 201 toactivate. For example, in one embodiment, processing circuit 201 maycause physiological sensor 230 to become activated based on determiningthat patient 110 has a certain risk, probability, or likelihood ofexperiencing a stroke (e.g., following an atrial fibrillation event). Inan embodiment, processing circuit 201 may cause physiological sensor 230to provide data more frequently, or may analyze data more frequently,based on determining that patient 110 has a certain risk, probability,or likelihood of experiencing a stroke. In an embodiment, processingcircuit 201 may change one or more parameters of a stroke determinationalgorithm (e.g., increasing the likelihood that an event such as a fall,a lack of motion, or slurred speech will be considered indicative of astroke) based on determining that patient 110 has a certain risk,probability, or likelihood of experiencing a stroke.

Input/output device 205 may include devices to receive inputs orinformation (e.g., buttons, keyboard, touchscreen, microphones, etc.) ordevices to provide outputs or information (e.g., speakers, displayscreen, lights, etc.). Input/output device 205 may be external fromstroke detection device 210, such as a mobile device, stereo system,television, computer, tablet computer, personal digital assistant(“PDA”), watch, etc. In some embodiments, stroke detection device 210may not include input/output device 205. When utilized with input/outputdevice 205, stroke detection device 210 may receive input frominput/output device 205 via communications interface 207 including, orin addition to, using a USB cable, Bluetooth technology, wirelesstechnology, etc.

In some embodiments, in addition to the inputs described above,processing circuit 201 may receive inputs via input/output device 205.For example, processing circuit 201 may receive an input frominput/output device 205 to selectively activate or deactivate processingcircuit 201. Input/output device 205 may be configured to receive inputsfrom patient 110, or other operator, to control settings of strokedetection device 210. Such settings may include, for example, high-alertor low-alert monitoring time periods. For example, patient 110 mayselectively configure stroke detection device 210 using input/outputdevice 205 to set an inactive period (e.g., when patient 110 plans to besleeping) in which stroke detection device 210 monitors patient 110 atless frequent intervals. In another example, patient 110 may selectivelyconfigure stroke detection device 210 using input/output device 205 toset a period of increased activity (e.g., when patient 110 plans to besleeping) in which stroke detection device 210 monitors patient 110 atmore frequent intervals.

Stroke detection device 210 is shown to include communications interface207. Communications interface 207 may include wired or wirelessinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith various systems, devices, or networks. For example, communicationsinterface 207 may include an Ethernet card and port for sending andreceiving data via an Ethernet-based communications network and/or aWi-Fi transceiver for communicating via a wireless communicationsnetwork. Communications interface 207 may be configured to communicatevia local area networks or wide area networks (e.g., the Internet, abuilding WAN, etc.) and may use a variety of communications protocols(e.g., BACnet, IP, LON, etc.).

Communications interface 207 may be a network interface configured tofacilitate electronic data communications between stroke detectiondevice 210 and various external systems or devices (e.g.,heart-monitoring sensor 220, physiological sensor 230, third-partymedical provider systems, medical monitoring service devices, etc.). Forexample, stroke detection device 210 may send information to a medicalprovider system indicating that patient 110 has experienced atrialfibrillation and has a certain likelihood of experiencing a strokeduring a period of time. Stroke detection device 210 may receive inputsfrom medical monitoring devices, emergency medical provider servicesystems, and sensors (e.g., heart-monitoring sensor 220, physiologicalsensor 230, etc.) via communications interface 207 and stroke detectiondevice 210 may provide inputs or control devices via communicationsinterface 207.

Heart-monitoring sensor 220 may include various types of sensors thatare configured to monitor the heart or a heart rhythm of patient 110.For example, heart-monitoring sensor 220 may include, or monitor a heartrhythm using, an electrocardiogram (i.e., ECG or EKG) or ultra widebandradar (e.g., micro-impulse radar). For example, micro-impulse radar maybe used to monitor a heart by bouncing radar off of a moving heart(i.e., beating heart), thereby monitoring the heart's motion. In someembodiments, heart-monitoring sensor 220 may be worn by patient 110, aswill be further described below. For example, patient 110 may wear anelectrocardiogram positioned over the heart (e.g., using a chest band,harness, halter monitor, etc.). Heart-monitoring sensor 220 may includea single electrode or a plurality of electrodes, which may be placed atvarious locations on patient 110 (e.g., over or near heart, on chest,arms, legs, etc.). Heart-monitoring sensor 220 may also monitor a heartrhythm using hemodynamic monitoring, which monitors the pressure andflow of blood within the circulatory system. In some embodiments,heart-monitoring sensor 220 is not worn by patient 110. For example,heart-monitoring sensor 220 may include a single sensor or a pluralityof sensors placed in proximity to patient 110 (e.g., installed on aceiling or wall, built-in or attached to an appliance, placed on atable, attached to a table, chair, bed, etc.).

Physiological sensor 230 may include various types of sensors that areconfigured to monitor a physiological characteristic of patient 110.Physiological sensor 230 may include microphones, cameras (e.g., videocameras, infrared cameras, etc.), keyboards (e.g., to detect typedphrases or ability to type), scanning devices, sonar, radar, motionsensors, pressure sensors (e.g., to measure blood pressure, etc.),thermometers, MRI devices, Doppler devices, eye tracking devices, amongothers. For example, microphones may monitor the language skills ormotor skills of patient 110. Cameras may monitor the level ofconsciousness of patient 110 (e.g., by detecting the orientation ofpatient 110, detecting a fall, etc.). Physiological sensor 230 may beconfigured to detect, monitor, or otherwise acquire inputs relating todiagnosing, determining, or processing whether patient 110 hasexperienced a stroke, is currently experiencing a stroke, or mayexperience a stroke, including sensors configured to detectcharacteristics of patient 110 that may be indicative of a stroke, suchas the level of consciousness, horizontal eye movement, visual fieldtests, facial palsy, movement of the limbs, limb ataxia, sensory tests,language skills, and motor skills, other physical and non-physicalcharacteristics of patient 110, and so on. For example, in oneembodiment, physiological sensor 230 may include a microphone configuredto monitor speech patterns of patient 110. In another example,physiological sensor 230 may include a pressure sensor configured tomonitor the cranial pressure of patient 110, including cranial bloodpressure, or blood pressure of patient 110 elsewhere in the body.Physiological sensor 230 may include sensors configured to detectcharacteristics of patient 110 using CT scans, MRI scans, Dopplerultrasound, and arteriography, among other techniques.

Referring to FIGS. 3A-3C, illustrations of a wearable stroke detectiondevice 310 are shown according to some embodiments. Stroke detectiondevice 310 may be similar to or the same as stroke detection device 210,as described above. Stroke detection device 310 may include elements inaddition to those described above with reference to stroke detectiondevice 210, for example to enable wearability or enhance mobility. Forexample, stroke detection device 310 may include wristbands, straps,Velcro, clips, fasteners, etc. so that stroke detection device 310 maybe worn by or carried by patient 110. In some embodiments, strokedetection device 310 may communicate with other devices, such as thoseshown in FIG. 1, including furniture mounted device 112 andwall-or-ceiling-mounted device 116. In some embodiments, strokedetection device 310 may be the same as or similar to wearable device114.

As shown in FIG. 3A, in one embodiment stroke detection device 310 isconfigured to be fastened to the wrist of patient 110. In thisembodiment, stroke detection device 310 may resemble a watch. Strokedetection device 310 may communicate (e.g., via communications interface207) with a sensor, such as heart-monitoring sensor 320, or a pluralityof sensors to, for example, receive inputs relating to a heartbeatrhythm of patient 110. For example, in one embodiment, heart-monitoringsensor 320 is affixed over the heart of patient 110 and sends input datato stroke detection device 310 using Bluetooth technology. In someembodiments, heart-monitoring sensor 320 may be the same asheart-monitoring sensor 220. In some embodiments, stroke detectiondevice 310 may further include a physiological sensor, such asphysiological sensor 230, as described above.

As shown in FIG. 3B, in one embodiment stroke detection device 310 isconfigured to be carried by patient 110. In this embodiment, strokedetection device 310 may resemble a mobile device, PDA, tablet computer,etc. Stroke detection device 310 may communicate (e.g., viacommunications interface 207) with a sensor, such as heart-monitoringsensor 320, or a plurality of sensors to, for example, receive inputsrelating to a heartbeat rhythm of patient 110. For example, in oneembodiment, heart-monitoring sensor 320 is worn as an arm-band over anarm of patient 110 and communicates with stroke detection device 310using a cord. In some embodiments, stroke detection device 310 mayfurther include a physiological sensor, such as physiological sensor230, as described above.

As shown in FIG. 3C, in one embodiment stroke detection device 310 isconfigured to be worn by patient 110 (e.g., over a torso, etc.). In thisembodiment, stroke detection device 310 may include heart-monitoringsensor 320. Stroke detection device 310 may communicate (e.g., viacommunications interface 207) with heart-monitoring sensor 320 andphysiological sensor 330 to, for example, receive inputs relating to aheartbeat rhythm or a physiological characteristic of patient 110. Forexample, in one embodiment, stroke detection device 310 is worn as achest wrap and communicates with physiological sensor 330 using awireless communications technology. In some embodiments, physiologicalsensor 330 may be the same as physiological sensor 230. In someembodiments, physiological sensor 330 may be selectively activatedand/or equipped by patient 110. For example, upon receiving anindication from stroke detection device 310 that patient 110 experiencedatrial fibrillation, patient 110 may equip or activate physiologicalsensor 330. In some embodiments, patient 110 may be instructed to equipor activate physiological sensor 330, or processing circuit 201 mayautomatically equip or activate physiological sensor 330 if processingcircuit 201 determines that the risk of patient 110 experiencing astroke has exceeded a threshold value. For example, in one embodiment,upon processing circuit 201 determining that patient 110 has a 40%likelihood of experiencing a stroke within the next four hours based onthe patient experiencing atrial fibrillation, processing circuit 201automatically activates physiological sensor 330.

Referring to FIG. 4, a block diagram of method 400 for detecting astroke is shown according to one embodiment. According to oneembodiment, method 400 is a computer-implemented method utilizing system200 and/or device 210 or any of the other devices disclosed herein. Forexample, method 400 may utilize any of the devices included in homesystem 100, including furniture mounted device 112, wearable device 114,and wall-or-ceiling-mounted device 116. Method 400 may be implementedusing any combination of computer hardware and software. According toone embodiment, the heart rhythm of a patient is monitored (401). Forexample, heart-monitoring sensor 220 may monitor the heart rhythm ofpatient 110. Next, a physiological change of the patient is detected(403). For example, physiological sensor 230 may detect a physiologicalchange in patient 110, such as a physiological change that is indicativeof a stroke, for example, slurred speech, falling down, change inconsciousness, change in cranial blood pressure, and so on. Next, adetermination is made as to whether the patient experienced a stroke(405). For example, determining whether the patient experienced a strokemay be based on the physiological change detected in step 403. If it isdetermined that patient 110 has not experienced a stroke, then method400 returns to monitoring the heart rhythm of patient 110 in step 401.Next, a determination is made as to whether patient 110 experiencedatrial fibrillation (407) (e.g., via heart data acquired byheart-monitoring sensor 220). For example, wall-or-ceiling-mounteddevice 116 may be configured to acquire heart data using micro-impulseradar. Next, a determination is made as to whether patient 110 likelyexperienced an embolic stroke (409). For example, processing circuit 201may determine that patient 110 experienced an embolic stroke based onpatient 110 experiencing atrial fibrillation within five hours ofexperiencing a stroke, and based on the magnitude and duration of thefibrillation (i.e., processing circuit 201 may determine that the factthe atrial fibrillation occurred increases the likelihood that asubsequently occurring stroke is an embolic stroke rather than ahemorrhagic stroke). If it is determined that patient 110 has notexperienced a stroke, then method 400 returns to monitoring the heartrhythm of patient 110 in step 401. If it is determined that patient 110likely experienced an embolic stroke at step 409, then an output isprovided that indicates that patient 110 experienced an embolic stroke(411). In some embodiments, an output may still be provided that patient110 experienced a stroke even if the stroke is not an embolic stroke. Itwill be appreciated that the order of the steps may vary, with stepsoccurring in a different order than that which they are discussed or asshown in the Figures. For example, in some embodiments, step 407 mayoccur between steps 401 and 403.

Referring to FIG. 5, a block diagram of method 500 for detecting astroke is shown according to another embodiment. According to oneembodiment, method 500 is a computer-implemented method utilizing system200 and/or device 210 or any of the other devices disclosed herein. Forexample, method 500 may utilize any of the devices included in homesystem 100, including furniture mounted device 112, wearable device 114,and wall-or-ceiling-mounted device 116. Method 500 may be implementedusing any combination of computer hardware and software. According toone embodiment, heart data is received (501). For example, heart datamay be received from heart-monitoring sensor 220. For example,wall-or-ceiling-mounted device 116 may be configured to acquire heartdata using micro-impulse radar. The heart data may further regard aheart rhythm of patient 110. The heart data may be indicative of anoccurrence of atrial fibrillation. Next, physiological data is received(503). For example, physiological data may be received fromphysiological sensor 230. The physiological data may regard aphysiological characteristic of patient 110. The physiological data maybe indicative of an occurrence of a stroke, for example, change incranial blood pressure. Next, a determination is made as to whether thepatient likely experienced an embolic stroke (505) (e.g., based on thephysiological data and heart data). Next, an output is provided thatincludes whether patient 110 experienced an embolic stroke (507). Forexample, the output may include sounds, visual indicators, and/or maytransmit to another device or system (e.g., an emergency medicalprovider). It will be appreciated that the order of the steps may vary,with steps occurring in a different order than that which they arediscussed or as shown in the Figures.

Referring to FIG. 6, a block diagram of method 600 for detecting astroke is shown according to one embodiment. According to oneembodiment, method 600 is a computer-implemented method utilizing system200 and/or device 210 or any of the other devices disclosed herein. Forexample, method 600 may utilize any of the devices included in homesystem 100, including furniture mounted device 112, wearable device 114,and wall-or-ceiling-mounted device 116. Method 600 may be implementedusing any combination of computer hardware and software. According toone embodiment, heart data is received which is indicative of anoccurrence of atrial fibrillation (601). For example, heart data may bereceived from heart-monitoring sensor 220. In one embodiment,furniture-mounted device 116 may be configured to acquire heart datausing micro-impulse radar. Next, physiological data is received which isindicative of an occurrence of a stroke (603). For example,physiological data may be received from physiological sensor 230. Next,the likelihood that patient 110 experienced an embolic stroke isdetermined based on the heart data (605). For example, processingcircuit 201 may determine that patient 110 experienced an embolic strokebased on patient 110 experiencing atrial fibrillation, falling down,and/or slurring their speech unlike their typical speaking pattern priorto experiencing atrial fibrillation and falling down. In someembodiments, an output may be provided based on the likelihood that thestroke was an embolic stroke (607). It will be appreciated that theorder of the steps may vary, with steps occurring in a different orderthan that which they are discussed or as shown in the Figures.

Referring to FIG. 7, a block diagram of method 700 for detecting amedical event is shown according to one embodiment. According to oneembodiment, method 700 is a computer-implemented method utilizing system200 and/or device 210 or any of the other devices disclosed herein. Forexample, method 700 may utilize any of the devices included in homesystem 100, including furniture mounted device 112, wearable device 114,and wall-or-ceiling-mounted device 116. Method 700 may be implementedusing any combination of computer hardware and software. According toone embodiment, a heart rhythm of a patient is monitored (701). Forexample, the heart rhythm of patient 110 may be monitored usingheart-monitoring sensor 220. Next, a physiological characteristic of thepatient is monitored (703). For example, the physiologicalcharacteristic may be monitored using physiological sensor 230. Next, adetermination is made as to whether the patient experienced a strokebased on the monitored physiological characteristic (705). Next, adetermination is made as to whether the patient experienced atrialfibrillation based on the monitored heart rhythm (707). Next, adetermination is made as to whether the patient experienced an embolicstroke based on the patient experiencing a stroke and atrialfibrillation within a predetermined time period (709). For example,processing circuit 201 may determine that patient 110 experienced anembolic stroke if patient 110 experienced atrial fibrillation within onehour of experiencing a stroke. Next, an output is provided (711) basedon the determination of whether the patient experienced an embolicstroke carried out in step 709. It will be appreciated that the order ofthe steps may vary, with steps occurring in a different order than thatwhich they are discussed or as shown in the Figures. For example, insome embodiments, step 707 may occur between steps 701 and 703.

Referring to FIG. 8, a block diagram of method 800 for determining aprobability of a patient experiencing a stroke is shown according to oneembodiment. According to one embodiment, method 800 is acomputer-implemented method utilizing system 200 and/or device 210 orany other devices disclosed herein. For example, method 800 may utilizeany of the devices included in home system 100, including furnituremounted device 112, wearable device 114, and wall-or-ceiling-mounteddevice 116. Method 800 may be implemented using any combination ofcomputer hardware and software. According to one embodiment, heart datais received (801). For example, heart data may be received fromheart-monitoring sensor 220. The heart data may be based on a monitoredheart rhythm of patient 110. The heart data may be indicative of patient110 experiencing atrial fibrillation. Next, a probability of the patientexperiencing a future stroke is determined based on the heart data(803). For example, processing circuit 201 may determine that patient110 has an 80% chance of experiencing a stroke within a one-hour timewindow, a 70% chance of experiencing a stroke within a two-hour timewindow, a 50% chance of experiencing a stoke within a three-hour timewindow, and less than a 25% chance of experiencing a stroke after fourhours based on received heart data, including heart data indicative ofatrial fibrillation, fibrillation magnitude, time between fibrillationmagnitudes of a certain threshold, and so on. In some embodiments, anoutput may be provided (805) based on the probability of patient 110experiencing a stroke. For example, a warning may be provided to patient110 if patient 110 has a 50% or greater chance of experiencing a strokeat any given time.

The construction and arrangement of the systems, methods, and devices asshown in the various embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the embodimentswithout departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedor modeled using existing computer processors, or by a special purposecomputer processor for an appropriate system, incorporated for this oranother purpose, or by a hardwired system. Embodiments within the scopeof the present disclosure include program products comprisingmachine-readable media for carrying or having machine-executableinstructions or data structures stored thereon. Such machine-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer or other machine with a processor.By way of example, such machine-readable media can comprise RAM, ROM,EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium which cancarry or store desired program code in the form of machine-executableinstructions or data structures and which can be accessed by a generalpurpose or special purpose computer or other machine with a processor.When information is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a machine, the machine properly views theconnection as a machine-readable medium. Thus, any such connection isproperly termed a machine-readable medium. Combinations of the above arealso included within the scope of machine-readable media.Machine-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing machines to perform a certain function orgroup of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Allsuch variations are within the scope of the disclosure. Likewise,software implementations could be accomplished with standard programmingtechniques with rule-based logic and other logic to accomplish thevarious connection steps, processing steps, comparison steps anddecision steps.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

1. A system for detecting a type of stroke, comprising: a processing circuit configured to: receive physiological data regarding a physiological characteristic of the patient, wherein the physiological data is indicative of an occurrence of a stroke; receive heart data regarding a heart rhythm of a patient, wherein the heart data is indicative of an occurrence of atrial fibrillation prior to the stroke; determine a likelihood that the stroke was an embolic stroke based on the heart data; and provide an output including an indication of the likelihood that the stroke was an embolic stroke.
 2. The system of claim 1, wherein the physiological data is based on a report from a human observer of the patient.
 3. The system of claim 1, wherein the physiological data is based on an image of the patient.
 4. (canceled)
 5. The system of claim 1, wherein the physiological data is based on an audio signal received from the patient.
 6. The system of claim 1, wherein the physiological data comprises a time associated with the occurrence of the stroke.
 7. The system of claim 6, wherein the time includes at least one of a time when the stroke started, a time when the physiological data was collected, and a time when the diagnosis that a stroke occurred was made.
 8. The system of claim 1, wherein the physiological data is based on a lack of motion of the patient.
 9. The system of claim 8, wherein the lack of motion is for a portion of the patient's body.
 10. The system of claim 8, wherein the lack of motion is for at least five minutes.
 11. The system of claim 1, wherein the physiological data includes an indication of a change in the patient's orientation.
 12. The system of claim 11, wherein the physiological data includes an indication of the patient falling down.
 13. The system of claim 1, wherein the physiological data includes an indication of a change in the patient's cranial pressure.
 14. The system of claim 1, wherein the physiological data includes an indication of a change in the patient's speech. 15-27. (canceled)
 28. The system of claim 1, wherein the processing circuit is further configured to control operation of an output device to provide the output to at least one of the patient and a medical provider. 29-60. (canceled)
 61. A system for detecting a type of stroke, comprising: a heart-monitoring device configured to acquire heart data regarding a heart rhythm of a patient, wherein the heart data is indicative of an occurrence of atrial fibrillation, and wherein the heart-monitoring device acquires heart data using micro impulse radar; and a processing circuit configured to: receive the heart data; receive physiological data regarding a physiological characteristic of the patient, wherein the physiological data is indicative of an occurrence of a stroke; determine a likelihood that the stroke was an embolic stroke based on the physiological data and the heart data; and control operation of an output device to provide the output, wherein the output is based on the likelihood that the stroke was an embolic stroke. 62-74. (canceled)
 75. The system of claim 61, further comprising a patient-monitoring sensor, wherein the patient-monitoring sensor is remote from the patient.
 76. The system of claim 75, wherein the patient-monitoring sensor is configured to acquire the physiological data using micro impulse radar.
 77. The system of claim 75, wherein the patient-monitoring sensor is configured to acquire the physiological data using a camera.
 78. The system of claim 75, wherein the patient-monitoring sensor is configured to acquire the physiological data using a microphone.
 79. The system of claim 61, further comprising a wearable heart-monitoring device configured to acquire the heart data.
 80. The system of claim 79, wherein the wearable heart-monitoring device further comprises an EKG monitor.
 81. The system of claim 61, wherein the processing circuit is further configured to determine a probability of the patient's heart emitting an embolus based on the heart data.
 82. The system of claim 61, wherein the likelihood that the stroke was an embolic stroke is further based on time data, wherein the time data is based on an amount of time elapsed between the patient experiencing atrial fibrillation and the stroke.
 83. The system of claim 61, wherein determination of the likelihood that the stroke was an embolic stroke is further based on a probability of the patient's heart emitting an embolus, wherein the probability of the patient's heart emitting the embolus is determined based on the heart data.
 84. The system of claim 80, wherein the heart data provides an indication of a fibrillation feature of the patient's heart.
 85. The system of claim 84, wherein the fibrillation feature provides an indication of at least one of a magnitude of the fibrillation, a duration of the fibrillation, and a number of episodes of fibrillation.
 86. (canceled)
 87. The system of claim 61, wherein the processing circuit is further configured to control operation of the output device to provide the output to at least one of the patient and a medical provider. 88-89. (canceled)
 90. The system of claim 61, wherein the hear-monitoring device is further configured to be accessed by a medical provider.
 91. The system of claim 61, wherein the output includes at least one of an audible output, tactile output, and a visual output.
 92. A method for detecting a stroke, comprising: receiving heart data regarding a heart rhythm of a patient, wherein the heart data is indicative of an occurrence of atrial fibrillation; receiving physiological data regarding a physiological characteristic of the patient, wherein the physiological data is indicative of an occurrence of a stroke after the occurrence of the atrial fibrillation; determining a likelihood that the stroke was an embolic stroke based on the physiological data and the heart data; and providing an output, wherein the output is based on the likelihood that the stroke was an embolic stroke. 93-94. (canceled)
 95. The method of claim 92, wherein the physiological data is based on radiofrequency radiation scattered from the patient. 96-114. (canceled)
 115. The method of claim 92, further comprising providing an output including an indication of the likelihood that the stroke was an embolic stroke.
 116. The method of claim 115, wherein providing the output is based on the likelihood that the stroke was an embolic stroke. 117-118. (canceled)
 119. The method of claim 115, wherein the output further includes the heart data.
 120. The method of claim 115, wherein the output further includes the physiological data. 121-258. (canceled) 